DevConf.cz 2021

OpenShift being a fast-growing open source application platform, makes deploying and managing machine learning models very easy and convenient. Since the development of machine learning models is an iterative procedure, many data scientists prefer to store their model source code like their Jupyter notebooks in Git so that they can perform frequent updates to their models. These models are then deployed via OpenShift in a production environment. To obtain the most optimized model, it is necessary for the models to be continuously retrained and deployed. How can we efficiently manage this periodic retraining and deployment of these machine learning models?

In this talk you will learn how to leverage DevOps for managing machine learning models on OpenShift. With the help of CI/CD tools like Tekton pipelines, we can now extend version control for data science applications as well. You will walk away from this talk knowing how to:
1. Train a machine learning model on an example use case
2. Maintain ML model code in Git
3. Setup CI/CD pipeline for your ML application